Improving privacy-preserving multi-faceted long short-term memory for accurate evaluation of encrypted time-series MRI images in heart disease - Čepová, Lenka; Elangovan, Muniyandy; Ramesh, Janjhyam Venkata Naga; Chohan, Mandeep Kaur; Verma, Amit; Mohammad, Faruq (Scientific reports, 2024)
In therapeutic diagnostics, early diagnosis and monitoring of heart disease is dependent on fast time-series MRI data processing. Robust encryption techniques are necessary to guarantee patient confidentiality. While deep learning (DL) algorithm have improved medical imaging, privacy and performance are still hard to balance. In this study, a novel approach for analyzing homomorphivally-encrypted (HE) time-series MRI data is introduced: The Multi-Faceted Long Short-Term Memory (MF-LSTM). This method includes privacy protection. The MF-LSTM architecture protects patient's privacy while accurately categorizing and forecasting cardiac disease, with accuracy (97.5%), precision (96.5%), recall (98.3%), and F1-score (97.4%). While segmentation methods help to improve interpretability by identifying important region in encrypted MRI images, Generalized Histogram Equalization (GHE) improves image quality. Extensive testing on selected dataset if encrypted time-series MRI images proves the method's stability and efficacy, outperforming previous approaches. The finding shows that the suggested technique can decode medical image to expose visual representation as well as sequential movement while protecting privacy and providing accurate medical image evaluation.
Keywords: Magnetic Resonance Imaging; Neural Networks, Computer; Female; důvěrnost informací; ženské pohlaví; nemoci srdce; Multi-faceted long short-term memory (MF-LSTM); Deep Learning; Computer Security; lidé; deep learning; magnetická rezonanční tomografie; Encryption; Heart Diseases; Algorithms; MRI Images; Male; počítačové zpracování obrazu; Confidentiality; neuronové sítě; soukromí; Heart Disease; zabezpečení počítačových systémů; algoritmy; lidé středního věku; mužské pohlaví; Privacy; Middle Aged; Image Processing, Computer-Assisted; Humans